
TaskManager
Manages tasks in a queue-based system where you can plan task lists, execute them one by one, and track completion status with unique IDs.
Manage and execute tasks efficiently with MCP TaskManager in a queue-based system. This server supports planning by accepting and organizing task lists, and execution by delivering tasks one at a time with feedback on completion. It tracks tasks via unique IDs, ensuring smooth workflow coordination through clear stages: planning, executing, and completing. Designed for integration with MCP clients like Claude Desktop, TaskManager simplifies complex task handling and improves automation by providing structured task queues and real-time updates. Its straightforward action parameters allow easy control over task flow, making it a powerful tool for effective task management.
What it does
- Plan and organize task lists from user requests
- Execute tasks sequentially from a queue
- Track task completion with approval workflow
- Update or delete incomplete tasks
- List all requests and their task status
- Add new tasks to existing requests
Best for
About TaskManager
TaskManager is a community-built MCP server published by kazuph that provides AI assistants with tools and capabilities via the Model Context Protocol. TaskManager streamlines project tracking and time management with efficient task queues, ideal for managing projects sof It is categorized under productivity. This server exposes 10 tools that AI clients can invoke during conversations and coding sessions.
How to install
You can install TaskManager in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
License
TaskManager is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Tools (10)
Register a new user request and plan its associated tasks. You must provide 'originalRequest' and 'tasks', and optionally 'splitDetails'. This tool initiates a new workflow for handling a user's request. The workflow is as follows: 1. Use 'request_planning' to register a request and its tasks. 2. After adding tasks, you MUST use 'get_next_task' to retrieve the first task. A progress table will be displayed. 3. Use 'get_next_task' to retrieve the next uncompleted task. 4. **IMPORTANT:** After marking a task as done, the assistant MUST NOT proceed to another task without the user's approval. The user must explicitly approve the completed task using 'approve_task_completion'. A progress table will be displayed before each approval request. 5. Once a task is approved, you can proceed to 'get_next_task' again to fetch the next pending task. 6. Repeat this cycle until all tasks are done. 7. After all tasks are completed (and approved), 'get_next_task' will indicate that all tasks are done and that the request awaits approval for full completion. 8. The user must then approve the entire request's completion using 'approve_request_completion'. If the user does not approve and wants more tasks, you can again use 'request_planning' to add new tasks and continue the cycle. The critical point is to always wait for user approval after completing each task and after all tasks are done, wait for request completion approval. Do not proceed automatically.
Given a 'requestId', return the next pending task (not done yet). If all tasks are completed, it will indicate that no more tasks are left and that you must wait for the request completion approval. A progress table showing the current status of all tasks will be displayed with each response. If the same task is returned again or if no new task is provided after a task was marked as done but not yet approved, you MUST NOT proceed. In such a scenario, you must prompt the user for approval via 'approve_task_completion' before calling 'get_next_task' again. Do not skip the user's approval step. In other words: - After calling 'mark_task_done', do not call 'get_next_task' again until 'approve_task_completion' is called by the user. - If 'get_next_task' returns 'all_tasks_done', it means all tasks have been completed. At this point, you must not start a new request or do anything else until the user decides to 'approve_request_completion' or possibly add more tasks via 'request_planning'.
Mark a given task as done after you've completed it. Provide 'requestId' and 'taskId', and optionally 'completedDetails'. After marking a task as done, a progress table will be displayed showing the updated status of all tasks. After this, DO NOT proceed to 'get_next_task' again until the user has explicitly approved this completed task using 'approve_task_completion'.
Once the assistant has marked a task as done using 'mark_task_done', the user must call this tool to approve that the task is genuinely completed. Only after this approval can you proceed to 'get_next_task' to move on. A progress table will be displayed before requesting approval, showing the current status of all tasks. If the user does not approve, do not call 'get_next_task'. Instead, the user may request changes, or even re-plan tasks by using 'request_planning' again.
After all tasks are done and approved, this tool finalizes the entire request. The user must call this to confirm that the request is fully completed. A progress table showing the final status of all tasks will be displayed before requesting final approval. If not approved, the user can add new tasks using 'request_planning' and continue the process.
MCP TaskManager
Model Context Protocol server for Task Management. This allows Claude Desktop (or any MCP client) to manage and execute tasks in a queue-based system.
Quick Start (For Users)
Prerequisites
- Node.js 18+ (install via
brew install node) - Claude Desktop (install from https://claude.ai/desktop)
Configuration
- Open your Claude Desktop configuration file at:
~/Library/Application Support/Claude/claude_desktop_config.json
You can find this through the Claude Desktop menu:
-
Open Claude Desktop
-
Click Claude on the Mac menu bar
-
Click "Settings"
-
Click "Developer"
-
Add the following to your configuration:
{
"tools": {
"taskmanager": {
"command": "npx",
"args": ["-y", "@kazuph/mcp-taskmanager"]
}
}
}
For Developers
Prerequisites
- Node.js 18+ (install via
brew install node) - Claude Desktop (install from https://claude.ai/desktop)
- tsx (install via
npm install -g tsx)
Installation
git clone https://github.com/kazuph/mcp-taskmanager.git
cd mcp-taskmanager
npm install
npm run build
Development Configuration
-
Make sure Claude Desktop is installed and running.
-
Install tsx globally if you haven't:
npm install -g tsx
# or
pnpm add -g tsx
- Modify your Claude Desktop config located at:
~/Library/Application Support/Claude/claude_desktop_config.json
Add the following to your MCP client's configuration:
{
"tools": {
"taskmanager": {
"args": ["tsx", "/path/to/mcp-taskmanager/index.ts"]
}
}
}
Available Operations
The TaskManager supports two main phases of operation:
Planning Phase
- Accepts a task list (array of strings) from the user
- Stores tasks internally as a queue
- Returns an execution plan (task overview, task ID, current queue status)
Execution Phase
- Returns the next task from the queue when requested
- Provides feedback mechanism for task completion
- Removes completed tasks from the queue
- Prepares the next task for execution
Parameters
action: "plan" | "execute" | "complete"tasks: Array of task strings (required for "plan" action)taskId: Task identifier (required for "complete" action)getNext: Boolean flag to request next task (for "execute" action)
Example Usage
// Planning phase
{
action: "plan",
tasks: ["Task 1", "Task 2", "Task 3"]
}
// Execution phase
{
action: "execute",
getNext: true
}
// Complete task
{
action: "complete",
taskId: "task-123"
}
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